Information exchange have changed processes associated with work and personal environments. Automation and improvements in processes have expanded scope of capabilities offered for personal and business data consumption. With the development of faster and smaller electronics, execution of mass processes at cloud systems have become feasible. Indeed, applications provided by data centers, data warehouses, data workstations have become common features in modern personal and work environments. Such systems provide a wide variety of services that manage and monitor feature deployment(s) associated with applications.
Increasingly, application management service(s) are utilized to manage a lifecycle of an application. The management service(s) deploy the application, monitor usage, manage feature update(s), and/or perform other tasks associated with the application. However, there are currently significant gaps when deploying a feature update based on an usage pattern. Personnel resources are unnecessarily consumed for predicting demand for the feature update. Lack of relevant feature demand inference schemes cause poor management of personnel resources when attempting to deploy a feature update for an application.